Genomic prediction and variance component estimation for carcass fat content in rainbow trout using SNP markers

Abstract

Specific locus amplified fragment sequencing-based genotyping was performed in rainbow trout, Oncorhynchus mykiss with 385 individuals from a freshwater breeding program in China to develop genome-wide single nucleotide polymorphism markers. Genomic prediction and variance component estimation were carried out for carcass fat content using genomic-relatedness-based best linear unbiased predictor (GBLUP) and genomic-relatedness-based restricted maximum likelihood methods. The 10-fold cross-validation analysis results showed that the genetic model including both additive and dominance effects had the best accuracy, and the observed predictive ability of the GBLUP estimates was 0.455 ± 0.050. Using this model to analyze the full data set of 385 individuals, the additive genomic heritability of carcass fat content was (Formula presented.) = 0.623 ± 0.175 with a negligible dominance effect. Our results suggested that the carcass fat content was mainly controlled by additive alleles. Thus, selective breeding could be performed based on the additive GBLUP values, and the methodology of genomic selection provides a reliable and feasible approach for optimizing carcass fat content of rainbow trout.